❗️⚠️ PROJECT DEPRECATED ⚠️❗️: Please try Wrinkl's successor SpecLinter https://github.com/orangebread/speclinter-mcp. Thanks to all who starred and supported this project!

Choose your preferred package manager:

# npm
npm install -g wrinkl
# pnpm
pnpm add -g wrinkl
# yarn
yarn global add wrinkl

# Initialize in your project
cd my-project
wrinkl init
# Create a feature ledger
wrinkl feature user-authentication
# List active features
wrinkl list
# Archive completed features
wrinkl archive user-authentication

🔥 ULTIMATE PROTIP: After running wrinkl init, ask your AI coding assistant to automatically populate the entire .ai/ directory for you! ✨

Wrinkl creates a .ai/ directory in your project with:

• 📄 Context files for AI assistants to understand your project
• 📚 Pattern documentation to maintain consistency
• 📋 Feature ledgers to track work progress
• 🏗️ Architecture decisions to guide development

• 🤖 Better AI Assistance - AI tools understand your project context
• 📝 Feature Tracking - Ledgers document progress and decisions
• 🎯 Pattern Consistency - Maintain coding standards across the team
• 🔄 Living Documentation - Context evolves with your project

Important: Keep your feedback loops tight! AI works better on focused tasks rather than sprawling features

"After 2+ years of coding exclusively with AI, I've learned that context is everything."

As a software engineer with 15 years of experience, I've witnessed the AI revolution transform how we build software. Wrinkl is my attempt to formalize the patterns and processes that make AI-assisted development truly effective.

The Problem: AI assistants are incredibly powerful, but they often lack the context needed to make decisions that align with your project's goals, patterns, and constraints.

The Solution: A structured approach to context management that keeps your AI assistants informed, your team aligned, and your codebase consistent.

This isn't just another tool—it's a methodology that evolves with the rapidly changing AI landscape.

💬 Want to chat about AI-assisted development? Hit me up on Discord: jayeeeffeff

After running wrinkl init, you'll have:

your-project/
├── .ai/
│ ├── README.md # Overview of the AI context system
│ ├── project.md # Project overview and requirements
│ ├── patterns.md # Coding patterns and conventions
│ ├── architecture.md # System architecture and decisions
│ ├── context-rules.md # Rules for AI assistants
│ └── ledgers/
│ ├── _active.md # Dashboard of active features
│ ├── _template.md # Template for new feature ledgers
│ ├── archived/ # Completed feature ledgers
│ └── [feature-name].md # Individual feature ledgers
├── .cursorrules # Cursor AI rules (optional)
├── augment.md # Augment AI context (optional)
└── .github/
 └── copilot-instructions.md # GitHub Copilot instructions (optional)

Initialize the AI context system in your project.

Options:

• -n, --name <name> - Project name (default: directory name)
• -t, --type <type> - Project type (default: "web app")
• -s, --stack <stack> - Technology stack (default: "TypeScript, Node.js")
• --no-cursor - Skip creating .cursorrules file
• --with-augment - Include augment.md file
• --with-copilot - Include GitHub Copilot instructions

Example:

wrinkl init --name "My App" --type "mobile app" --stack "React Native, Node.js"

Create a new feature ledger to track development progress.

Example:

wrinkl feature user-authentication

List all active feature ledgers and their status.

Options:

• -a, --all - Include archived features

Example:

wrinkl list --all

Archive a completed feature ledger.

Example:

wrinkl archive user-authentication

The .ai/project.md file contains your project's core information:

• Project goals and constraints
• Technology stack
• Key requirements
• Development workflow

The .ai/patterns.md file documents:

• Code style and conventions
• Common patterns and anti-patterns
• Testing strategies
• Performance guidelines

The .ai/architecture.md file captures:

• System design decisions
• Technology choices and trade-offs
• Scalability considerations
• Security architecture

Individual feature files track:

• Feature requirements and goals
• Technical approach and decisions
• Progress updates and blockers
• Testing and deployment notes

The .ai/context-rules.md file provides:

• Guidelines for AI assistants
• Code quality standards
• Security and performance rules
• Project-specific requirements

1. Keep context updated 🔄 - Regularly update project files as requirements change
2. Use feature ledgers 📝 - Create a ledger for each significant feature
3. Document decisions 📋 - Record important technical decisions in ledgers
4. Review patterns 🔍 - Regularly review and update coding patterns

1. Reference context 📖 - Tell AI assistants to read the .ai/ directory
2. Mention features 🎯 - Reference specific feature ledgers when working
3. Update progress ⏱️ - Keep ledgers updated with progress and decisions
4. Follow patterns ✅ - Ensure AI-generated code follows project patterns

"I'm working on the user-authentication feature. Please read the feature
ledger in .ai/ledgers/user-authentication.md and help me implement the
login component following the patterns in .ai/patterns.md"

"Please review the project context in .ai/project.md and suggest an
architecture for the new notification system, documenting your decisions
in a new feature ledger"

If you use Cursor, the .cursorrules file provides context and guidelines for the AI assistant.

If you use Windsurf, simply rename .cursorrules to .windsurfrules - the content is identical, just different filename conventions.

The augment.md file provides context for Augment AI when working on your project.

The .github/copilot-instructions.md file guides GitHub Copilot to generate code that follows your project patterns.

1. 🍴 Fork the repository
2. 🌿 Create a feature branch
3. ✏️ Make your changes
4. 🧪 Add tests
5. 📤 Submit a pull request

MIT - see LICENSE file for details.

• 🐛 GitHub Issues